With the development of mobile Internet, a growing number of users access the Internet by using intelligent terminal devices. The development of mobile Internet applications represented by a social networking service and instant messaging brings about an input requirement that is much higher than a traditional one.
Currently, a technical method such as a preset information template technology, a word association input technology, a word input recommendation and correction technology based on an individual language model (Individual Language Model), or improvement in convenience of an editing function by means of UI (User Interface) design is generally used.
For the preset information template technology, a common characteristic of such a technology is as follows: word segmentation and classification processing are performed on to-be-replied content, and then reply content is recommended according to a set rule mode. A specific implementation solution may be based on a terminal, based on a network side service, or based on a terminal and a network. In such a solution, recommendation is performed only according to matching with a keyword and a given rule, which causes relatively low accuracy of a model.
For the word association input technology, a characteristic of such a technology is as follows: a character, a word, a phrase, or a sentence is rearranged according to frequency of use to form a lexicon, keyboard input of a user is used as a search condition, and multiple preferred options are found from the lexicon for the user to select; in some technologies, most recent input (for example, a previous character or word) is also included into the search condition; and such a solution also provides a fault tolerance feature to some extent. However, a linguistic characteristic of the user cannot be reflected, and only a word group and a phrase can be associated, so that improvement in input convenience is limited.
For an input technology based on an individual language model, in such a technology, an individual language model of a user is trained based on a historical input record of the user, and input of the user is forecasted and corrected based on the foregoing individual language model by using the Bayesian method. In such a method, a forecast and a correction are performed by using input of a user, but recommended reply information suitable for a context cannot be provided.
Currently, in some fuzzy matching technologies used by a search engine, possible search content is forecasted and recommended according to user input, and a user input interface is improved by using human-computer interaction techniques. Similar to an existing input method technology based on an individual language model, these technologies can improve input convenience for a user to some extent, but none of them can provide recommended reply information suitable for a context.
With transition of terminal use from simple address book query and short message input to complex scenarios such as instant messaging, social interaction, and even blog writing, the foregoing technologies become increasingly difficult to quickly and accurately provide recommended reply information suitable for a context when a user replies to information.